MACHINE LEARNING LAB

Paper Code: 
MCA 422C
Credits: 
02
Periods/week: 
04
Max. Marks: 
100.00
Objective: 

Course Objectives:

The course enables the students to

  1. Apply algorithms on appropriate data sets.
  2. Analyze various algorithms of machine learning.
  3. Evaluate performance of learning algorithms based on same techniques
  4. Create machine learning based solutions to solve real world problems.

 

Course Outcomes(COs):

Learning Outcome (at course level)

Learning and teaching strategies

Assessment Strategies

CLO181. Apply algorithms on appropriate data sets.

CLO182. Analyze different algorithms of machine learning

CLO183. Evaluate performances of different algorithms 

CLO184. Create machine learning based solutions for real world problems.

Approach in teaching:

Interactive Lab Sessions, Modeling, Discussions, implementing enquiry based learning, student centered

approach

 

Learning activities for the students:

Experiential Learning,

Discussions, Lab Assignments, Learning through Real life data centric problems

  • Lab Assignments
  • Practical Record
  • Continuous Assessment
  • Semester End Examination

 

Contents

  1. Implement and demonstrate the FIND-S algorithm for finding the most specific hypothesis based on a given set of training samples. Read the training data from a .csv file.
  2. Implement working of the decision tree based ID3 algorithm using appropriate data set to classify it.
  3. Develop an Artificial Neural Network by implementing the Back propagation algorithm and test the same using appropriate data set.  
  4. Implement the naïve Bayesian classifier using appropriate data set and compute its accuracy, considering few data sets.
  5. Implement Bayesian network considering medical data. Use this model to demonstrate the diagnosis of Heart Disease Data Set.
  6. Implement EM algorithm to cluster a set of data stored in a .CSV file.
  7. Implement k-means algorithm to cluster same set of data as in experiment 6 and compare the results of these two algorithms and comment on the quality of clustering.
  8. Implement k-Nearest Neighbor algorithm to classify the iris data set and display both correct and incorrect predictions.
  9. Implement the non-parametric Locally Weighted Regression algorithm in order to fit data points. Apply it on an appropriate data set and draw graph.  

 

Academic Year: 
2021-22
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